How Big Data Is Driving Companies

Top 10 Business Intelligence

Buzzwords for 2019

Data Security Data Science

Predictive Analytics

ContinuousNext

Chatbots Mobile Analytics

DevOps Trinity Chief Analytics Officer

Digital Automation

Digital Citizen

Businesses lack real time visibility into the quality of consumption of digital assets resulting in poor internal and external user experience and satisfaction.

REASONS TO BELIEVE IN BIG DATA

• Over 50% of C-Suite executives recently surveyed believe big data is a game changer

• For the first time in history, companies have tools to harness internal data and use it

• These tools give insight into customers, markets, trends and opportunities

• Uncovering the patterns provides for predictive analysis

• Using big data improved efficiency and decision making

BUSINESS INTELLIGENCE

Right data. Right people. Right time. Transforming data into actions that drive

revenue, streamline operational efficiency

and improve the overall customer

experience

Connected Analytics

Behavioral Analytics

Connected Applications

TYPES OF ANALYTICSWhat is data telling you?

Descriptive: What happened in my business?• Comprehensive, accurate and live data• Effective visualization

Diagnostic: Why did it happen?• Ability to drill down to the root-cause• Ability to isolate all confounding information

Predictive: What’s likely to happen?• Business strategies have remained fairly consistent over

time• Historical patterns being used to predict specific outcomes

using algorithms• Decisions are automated using algorithms and technology

Prescriptive: What do I need to do?• Recommended actions and strategies based on outcomes• Applying advanced analytical techniques to make specific

recommendations

LEVERAGING DATA FOR SUCCESS

Information that doesn’t help increase revenues or decrease costs is simply overhead and is irrelevant to your goals.

Data and Data Analytics provide the Underpinning for Effective Digital Transformation

DIGITAL TRANSFORMATION/IMPLEMENTATIONResult was transformation of fulfillment operations with implementationof multi-node fulfillment configuration delivering savings of $4-5M inshipping costs annually, saving shipping time and reducing carbonemissions

ANALYTICS/PROBLEM SOLVINGAnalytics used for intelligent reconciliation between inbound ordersystems, order hub, fulfillment hub and BI System so all orders are trackedand accounted for in stages of order processing

OPERATIONAL IMPACTSData was leveraged to gain insights (BI) into fulfillment operations anduncover opportunities to transform the supply chain

DATA COLLECTION/VISUALIZATIONData related to customer orders, shipment destinations, distributioncenters, inventory availability and shipping logistics

TACTICALOPERATIONAL STRATEGIC

BUSINESS INTELLIGENCE

OPERATIONAL ANALYTICS

Customer metrics• Behavioral analytics

Real estate analysis

Use of cellular data

Strategic planning

“Push” methodology of data

WiFi capture• Customer demographic data• Pre/post visit marketing• Camera utilization

Market conditions• Renewals• Relocations• Growth • Trends

Comprehensive utilization of data

RE portfolio/OptimizationOperations/Staff scorecardCustomer retention

Automated data pulling from data sources• Frequency• Reporting

Challenges Solutions

KPI’S, METRICS AND BUSINESS ANALYTICS

Business Intelligence Reports and Pivot tables Indicators, Metricsand Benchmarks

Graphics and VisualizationsDashboards Analytics

REAL ESTATE ASSET MANAGEMENT

Portfolio Management & Optimization

Provides real-time organization and visualization of portfolio, client, market and enterprise data for informed decisions

BUSINESS INTELLIGENCE

Site Selector-Micro Multi-layered data analysis to validate areas for renewals, relocations, expansions or consolidations

BEHAVIORAL ANALYTICS/ EX. RETAILERS

Retailers want to understand their customer

behavior, and sense and shape demand.

Traditional solutions to understanding

customer behavior rely on post customer visit

analytics that make it impossible to market to

the customer while they are at the store.

They lack visibility into what the demographic

of the customer entering the store in various

geographies at various times of the year.

o Gone are days where you had to rely on

sales data to draw limited conclusions

BEHAVIORAL ANALYTICS/ EX. RETAILERS

Use of camera feeds to detect faces, predict demographics, elicit emotions and draw

correlations.

Machine Learning algorithms assign identifier to each face using a matrix of data

points based on the curvature of the face.

Customers can be identified across various zones within the store

and across stores.

Dwell times and traffic patterns inform product placement choices

increasing revenue.

Customer Service can be improved by detecting and addressing

customers' needs.

HOW?

BEHAVIORAL ANALYTICS/ EX. RETAILERS

Natural Language Understanding (NLU) allows companies to convert speech to text and vice versa.

Using this technology, customers can self serve using Alexa/Siri type of interaction with kiosks in retail and hospitality.

Big Data and Machine Learning make processing huge amount of video and voice feeds possible on the edge and in the cloud.

https://coreplus.net/

HOW?

Big Data has enabled enterprises to collect massive amounts of consumer data and that has raised privacy concerns and lead to regulations such as GDPR (General Data Protection Regulation) in the EU. Consumers "Right to Forget” has become a huge compliance need for Enterprise Software.

California Consumer Privacy Act (CCPA) is another such compliance regulation that is going into effect January 1st, 2020. The compliance requirements require developers to make architectural provisions to not collect, anonymize and erase data as needed.

Educational institutions have to introduce programs to educate students about Data Protection and Compliance along with Big Data Analytics and Data Science.

REGULATIONS/ PRIVACY CONCERNS